{"id":1176,"date":"2023-07-27T09:36:52","date_gmt":"2023-07-27T09:36:52","guid":{"rendered":"https:\/\/statorials.org\/ar\/k-%d8%a7%d9%94%d8%b6%d8%b9%d8%a7%d9%81-%d8%a7%d9%84%d8%aa%d8%ad%d9%82%d9%82-%d9%85%d9%86-%d8%a7%d9%84%d8%b5%d8%ad%d8%a9-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/"},"modified":"2023-07-27T09:36:52","modified_gmt":"2023-07-27T09:36:52","slug":"k-%d8%a7%d9%94%d8%b6%d8%b9%d8%a7%d9%81-%d8%a7%d9%84%d8%aa%d8%ad%d9%82%d9%82-%d9%85%d9%86-%d8%a7%d9%84%d8%b5%d8%ad%d8%a9-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/k-%d8%a7%d9%94%d8%b6%d8%b9%d8%a7%d9%81-%d8%a7%d9%84%d8%aa%d8%ad%d9%82%d9%82-%d9%85%d9%86-%d8%a7%d9%84%d8%b5%d8%ad%d8%a9-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/","title":{"rendered":"\u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0635\u062d\u0629 k-fold \u0641\u064a python (\u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629)"},"content":{"rendered":"

<\/p>\n

\n

\u0644\u062a\u0642\u064a\u064a\u0645 \u0623\u062f\u0627\u0621 \u0646\u0645\u0648\u0630\u062c \u0645\u0627 \u0639\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a\u060c \u0646\u062d\u062a\u0627\u062c \u0625\u0644\u0649 \u0642\u064a\u0627\u0633 \u0645\u062f\u0649 \u0645\u0637\u0627\u0628\u0642\u0629 \u0627\u0644\u062a\u0646\u0628\u0624\u0627\u062a \u0627\u0644\u062a\u064a \u0642\u062f\u0645\u0647\u0627 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0645\u0639 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0645\u0631\u0635\u0648\u062f\u0629.<\/span><\/p>\n

\u062a\u064f\u0639\u0631\u0641 \u0627\u0644\u0637\u0631\u064a\u0642\u0629 \u0634\u0627\u0626\u0639\u0629 \u0627\u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0644\u0644\u0642\u064a\u0627\u0645 \u0628\u0630\u0644\u0643 \u0628\u0627\u0633\u0645 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0642\u0627\u0637\u0639 k-fold<\/a> \u060c \u0648\u0627\u0644\u0630\u064a \u064a\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0637\u0631\u064a\u0642\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/span><\/p>\n

1.<\/strong> \u0642\u0645 \u0628\u062a\u0642\u0633\u064a\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0639\u0634\u0648\u0627\u0626\u064a\u064b\u0627 \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a k<\/em> \u060c \u0623\u0648 “\u0637\u064a\u0627\u062a”\u060c \u0630\u0627\u062a \u062d\u062c\u0645 \u0645\u062a\u0633\u0627\u0648\u064d \u062a\u0642\u0631\u064a\u0628\u064b\u0627.<\/span><\/p>\n

2.<\/strong> \u0627\u062e\u062a\u0631 \u0625\u062d\u062f\u0649 \u0627\u0644\u0637\u064a\u0627\u062a \u0643\u0645\u062c\u0645\u0648\u0639\u0629 \u0636\u0628\u0637 \u0627\u0644\u0646\u0641\u0633. \u0627\u0636\u0628\u0637 \u0627\u0644\u0642\u0627\u0644\u0628 \u0639\u0644\u0649 \u0637\u064a\u0627\u062a k-1 \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629. \u062d\u0633\u0627\u0628 \u0627\u062e\u062a\u0628\u0627\u0631 MSE \u0639\u0644\u0649 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0641\u064a \u0627\u0644\u0637\u0628\u0642\u0629 \u0627\u0644\u062a\u064a \u062a\u0645 \u0634\u062f\u0647\u0627.<\/span><\/p>\n

3.<\/strong> \u0643\u0631\u0631 \u0647\u0630\u0647 \u0627\u0644\u0639\u0645\u0644\u064a\u0629 \u0639\u062f\u0629<\/em> \u0645\u0631\u0627\u062a\u060c \u0641\u064a \u0643\u0644 \u0645\u0631\u0629 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0645\u062e\u062a\u0644\u0641\u0629 \u0643\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0627\u0633\u062a\u0628\u0639\u0627\u062f.<\/span><\/p>\n

4.<\/strong> \u0627\u062d\u0633\u0628 \u0627\u062e\u062a\u0628\u0627\u0631 MSE \u0627\u0644\u0625\u062c\u0645\u0627\u0644\u064a \u0643\u0645\u062a\u0648\u0633\u0637 \u0644\u0627\u062e\u062a\u0628\u0627\u0631 k<\/em> MSEs.<\/span><\/p>\n

\u064a\u0642\u062f\u0645 \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0645\u062b\u0627\u0644\u0627\u064b \u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629 \u0644\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0642\u0627\u0637\u0639 k-fold \u0644\u0646\u0645\u0648\u0630\u062c \u0645\u0639\u064a\u0646 \u0641\u064a Python.<\/span><\/p>\n

\u0627\u0644\u062e\u0637\u0648\u0629 1: \u062a\u062d\u0645\u064a\u0644 \u0627\u0644\u0645\u0643\u062a\u0628\u0627\u062a \u0627\u0644\u0636\u0631\u0648\u0631\u064a\u0629<\/strong><\/span><\/h3>\n

\u0623\u0648\u0644\u0627\u064b\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u062a\u062d\u0645\u064a\u0644 \u0627\u0644\u0648\u0638\u0627\u0626\u0641 \u0648\u0627\u0644\u0645\u0643\u062a\u0628\u0627\u062a \u0627\u0644\u0644\u0627\u0632\u0645\u0629 \u0644\u0647\u0630\u0627 \u0627\u0644\u0645\u062b\u0627\u0644:<\/span><\/p>\n

 from<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split\nfrom<\/span> sklearn. model_selection<\/span> import<\/span> KFold\nfrom<\/span> sklearn. model_selection<\/span> import<\/span> cross_val_score\nfrom<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\nfrom<\/span> numpy import<\/span> means\nfrom<\/span> numpy import<\/span> absolute\nfrom<\/span> numpy import<\/span> sqrt\nimport<\/span> pandas as<\/span> pd\n<\/strong><\/span><\/pre>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 2: \u0625\u0646\u0634\u0627\u0621 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a<\/strong><\/span><\/h3>\n
 \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0625\u0637\u0627\u0631 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0627\u0644\u0630\u064a \u064a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0645\u062a\u063a\u064a\u0631\u064a\u0646 \u0645\u062a\u0648\u0642\u0639\u064a\u0646\u060c x1<\/sub> \u0648 x2<\/sub> \u060c \u0648\u0645\u062a\u063a\u064a\u0631 \u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0648\u0627\u062d\u062f y.<\/span><\/p>\n
 df = pd.DataFrame({' y<\/span> ': [6, 8, 12, 14, 14, 15, 17, 22, 24, 23],\n                   ' x1<\/span> ': [2, 5, 4, 3, 4, 6, 7, 5, 8, 9],\n                   ' x2<\/span> ': [14, 12, 12, 13, 7, 8, 7, 4, 6, 5]})\n<\/strong><\/span><\/pre>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 3: \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0635\u062d\u0629 K-Fold<\/strong><\/span><\/h3>\n
 \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0646\u0644\u0627\u0626\u0645 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f<\/a> \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0648\u0646\u0646\u0641\u0630 LOOCV \u0644\u062a\u0642\u064a\u064a\u0645 \u0623\u062f\u0627\u0621 \u0627\u0644\u0646\u0645\u0648\u0630\u062c.<\/span><\/p>\n
 #define predictor and response variables\n<\/span>X = df[[' x1<\/span> ', ' x2<\/span> ']]\ny = df[' y<\/span> ']\n\n#define cross-validation method to use\n<\/span>cv<\/span> =<\/span> KFold<\/span> (<\/span> n_splits<\/span> =<\/span> 10<\/span> ,<\/span> random_state<\/span> =<\/span> 1<\/span> ,<\/span> shuffle<\/span> =<\/span> True<\/span> )<\/span>\n\n#build multiple linear regression model\n<\/span>model = LinearRegression()\n\n#use k-fold CV to evaluate model\n<\/span>scores = cross_val_score(model, X, y, scoring=' neg_mean_absolute_error<\/span> ',\n                         cv=cv, n_jobs=-1)\n\n#view mean absolute error\n<\/span>mean(absolute(scores))\n\n3.6141267491803646\n<\/strong><\/span><\/pre>\n
 \u0645\u0646 \u0627\u0644\u0646\u062a\u064a\u062c\u0629 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u0645\u062a\u0648\u0633\u0637 \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0645\u0637\u0644\u0642 (MAE) \u0643\u0627\u0646 3.614<\/strong> . \u0623\u064a \u0623\u0646 \u0645\u062a\u0648\u0633\u0637 \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0645\u0637\u0644\u0642 \u0628\u064a\u0646 \u0627\u0644\u062a\u0646\u0628\u0624 \u0627\u0644\u0646\u0645\u0648\u0630\u062c\u064a \u0648\u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0645\u0631\u0635\u0648\u062f\u0629 \u0641\u0639\u0644\u064a\u0627\u064b \u0647\u0648 3.614.<\/span><\/p>\n
 \u0628\u0634\u0643\u0644 \u0639\u0627\u0645\u060c \u0643\u0644\u0645\u0627 \u0627\u0646\u062e\u0641\u0636 MAE\u060c \u0643\u0644\u0645\u0627 \u0643\u0627\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0642\u0627\u062f\u0631\u064b\u0627 \u0639\u0644\u0649 \u0627\u0644\u062a\u0646\u0628\u0624 \u0628\u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0627\u0644\u0641\u0639\u0644\u064a\u0629 \u0628\u0634\u0643\u0644 \u0623\u0641\u0636\u0644.<\/span><\/p>\n
 \u0645\u0642\u064a\u0627\u0633 \u0622\u062e\u0631 \u0634\u0627\u0626\u0639 \u0627\u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0644\u062a\u0642\u064a\u064a\u0645 \u0623\u062f\u0627\u0621 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0647\u0648 \u062c\u0630\u0631 \u0645\u062a\u0648\u0633\u0637 \u0645\u0631\u0628\u0639 \u0627\u0644\u062e\u0637\u0623 (RMSE). \u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u062d\u0633\u0627\u0628 \u0647\u0630\u0627 \u0627\u0644\u0645\u0642\u064a\u0627\u0633 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 LOOCV:<\/span><\/p>\n
 #define predictor and response variables\n<\/span>X = df[[' x1<\/span> ', ' x2<\/span> ']]\ny = df[' y<\/span> ']\n\n#define cross-validation method to use\n<\/span>cv<\/span> =<\/span> KFold<\/span> (<\/span> n_splits<\/span> =<\/span> 5<\/span> ,<\/span> random_state<\/span> =<\/span> 1<\/span> ,<\/span> shuffle<\/span> =<\/span> True<\/span> )<\/span> \n\n#build multiple linear regression model\n<\/span>model = LinearRegression()\n\n#use LOOCV to evaluate model\n<\/span>scores = cross_val_score(model, X, y, scoring=' neg_mean_squared_error<\/span> ',\n                         cv=cv, n_jobs=-1)\n\n#view RMSE\n<\/span>sqrt(mean(absolute(scores)))\n\n4.284373111711816<\/strong><\/span><\/pre>\n
 \u0645\u0646 \u0627\u0644\u0646\u062a\u064a\u062c\u0629 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u062c\u0630\u0631 \u0645\u062a\u0648\u0633\u0637 \u0645\u0631\u0628\u0639 \u0627\u0644\u062e\u0637\u0623 (RMSE) \u0643\u0627\u0646 4.284<\/strong> .<\/span><\/p>\n
 \u0643\u0644\u0645\u0627 \u0627\u0646\u062e\u0641\u0636 RMSE\u060c \u0643\u0644\u0645\u0627 \u0643\u0627\u0646\u062a \u0642\u062f\u0631\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0639\u0644\u0649 \u0627\u0644\u062a\u0646\u0628\u0624 \u0628\u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0627\u0644\u0641\u0639\u0644\u064a\u0629 \u0623\u0641\u0636\u0644.<\/span><\/p>\n
 \u0645\u0646 \u0627\u0644\u0646\u0627\u062d\u064a\u0629 \u0627\u0644\u0639\u0645\u0644\u064a\u0629\u060c \u0646\u062d\u0646 \u0639\u0627\u062f\u0629\u064b \u0646\u0644\u0627\u0626\u0645 \u0639\u062f\u0629 \u0646\u0645\u0627\u0630\u062c \u0645\u062e\u062a\u0644\u0641\u0629 \u0648\u0646\u0642\u0627\u0631\u0646 RMSE \u0623\u0648 MAE \u0644\u0643\u0644 \u0646\u0645\u0648\u0630\u062c \u0644\u062a\u062d\u062f\u064a\u062f \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0630\u064a \u064a\u0646\u062a\u062c \u0623\u0642\u0644 \u0645\u0639\u062f\u0644\u0627\u062a \u062e\u0637\u0623 \u0641\u064a \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0648\u0628\u0627\u0644\u062a\u0627\u0644\u064a \u0641\u0647\u0648 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0623\u0641\u0636\u0644 \u0644\u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645.<\/span><\/p>\n
 \u0644\u0627\u062d\u0638 \u0623\u064a\u0636\u064b\u0627 \u0623\u0646\u0647 \u0641\u064a \u0647\u0630\u0627 \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u062e\u062a\u0631\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 k=5 \u0637\u064a\u0627\u062a\u060c \u0648\u0644\u0643\u0646 \u064a\u0645\u0643\u0646\u0643 \u0627\u062e\u062a\u064a\u0627\u0631 \u0623\u064a \u0639\u062f\u062f \u0645\u0646 \u0627\u0644\u0637\u064a\u0627\u062a \u0627\u0644\u062a\u064a \u062a\u0631\u064a\u062f\u0647\u0627.<\/span><\/p>\n
 \u0645\u0646 \u0627\u0644\u0646\u0627\u062d\u064a\u0629 \u0627\u0644\u0639\u0645\u0644\u064a\u0629\u060c \u0646\u062e\u062a\u0627\u0631 \u0639\u0627\u062f\u0629\u064b \u0645\u0627 \u0628\u064a\u0646 5 \u0625\u0644\u0649 10 \u0637\u0628\u0642\u0627\u062a\u060c \u062d\u064a\u062b \u064a\u062b\u0628\u062a \u0623\u0646 \u0647\u0630\u0627 \u0647\u0648 \u0627\u0644\u0639\u062f\u062f \u0627\u0644\u0623\u0645\u062b\u0644 \u0644\u0644\u0637\u0628\u0642\u0627\u062a \u0627\u0644\u062a\u064a \u062a\u0646\u062a\u062c \u0645\u0639\u062f\u0644\u0627\u062a \u062e\u0637\u0623 \u0645\u0648\u062b\u0648\u0642\u0629 \u0641\u064a \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631.<\/span><\/p>\n
 \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h3>\n
 \u0645\u0642\u062f\u0645\u0629 \u0644\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0635\u062d\u0629 K-Fold<\/a>
 \u062f\u0644\u064a\u0644 \u0643\u0627\u0645\u0644 \u0644\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a>
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